The CUHK-TUDELFT System for The SLT 2021 Children Speech Recognition Challenge
Not on the map. Why?
Abstract (arXiv)
This technical report describes our submission to the 2021 SLT Children Speech Recognition Challenge (CSRC) Track 1. Our approach combines the use of a joint CTC-attention end-to-end (E2E) speech recognition framework, transfer learning, data augmentation and development of various language models. Procedures of data pre-processing, the background and the course of system development are described. The analysis of the experiment results, as well as the comparison between the E2E and DNN-HMM hybrid system are discussed in detail. Our system achieved a character error rate (CER) of 20.1% in our designated test set, and 23.6% in the official evaluation set, which is placed at 10-th overall.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Nov 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 2; 2024: 1.
3 papers.
- 2024Automatic Speech Recognition using Advanced Deep Learning Approaches: A surveycited by 50
- 2023Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalizationcited by 28
- 2023Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systemscited by 2
- 2024Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey2 Mar 2024
- 2023Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems5 Jul 2023
- 2023Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization27 Apr 2023